Deep Generative Models
Fee may apply
Generative models are a key paradigm for probabilistic reasoning within graphical models and probabilistic programming languages. It is one of the exciting and rapidly-evolving fields of statistical machine learning and artificial intelligence. Recent advances in parameterizing generative models using deep neural networks, combined with progress in stochastic optimization methods, have enabled scalable modeling of complex, high-dimensional data including images, text, and speech. In this course, we will study the probabilistic foundations and learning algorithms for deep generative models and discuss application areas that have benefitted from deep generative models.
What you will learn
- A powerful way of learning data distribution
- How to apply various algorithms to decision making, finding analogies, and predicting future events
- Various applications to deep generative models including computer vision, speech and language processing
Basic knowledge about machine learning from at least one of CS 221, 228, 229 or 230. Students will work with computational and mathematical models and should have a basic knowledge of probabilities and calculus. Proficiency in some programming language, preferably Python, required.
- Autoregressive models
- Variational autoencoders
- Normalizing flow models
- Generative adversarial networks
- Energy-based models
Note on Course Availability
This course is typically offered Autumn quarter.
The course schedule is displayed for planning purposes – courses can be modified, changed, or cancelled. Course availability will be considered finalized on the first day of open enrollment. For quarterly enrollment dates, please refer to our graduate certificate homepage.
|Dates:||September 23 - December 6, 2019|
Provides Stanford University credit that may later be applied towards a graduate degree or certificate. Includes access to online course materials and videos for the duration of the academic quarter. Starting Autumn 2016 there is a $100 fee per course for courses dropped before the drop deadline. Click here for more information about our Registration Policies.
NotesEnrollment Dates: August 1 to September 9, 2019
Computer Science Department Requirement
Students taking graduate courses in Computer Science must enroll for the maximum number of units and maintain a B or better in each course in order to continue taking courses under the Non Degree Option.
This course may not currently be available to learners in some states and territories.